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Record W2494217926 · doi:10.3390/jmse4030046

Application of a Spectral Wave Model to Assess Breakwater Configurations at a Small Craft Harbour on Lake Ontario

2016· article· en· W2494217926 on OpenAlexafffundabout
Amelia Cooper, Ryan P. Mulligan

Bibliographic record

VenueJournal of Marine Science and Engineering · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBreakwaterHarbourWave modelGeologyWave heightAttenuationStormWind waveSignificant wave heightEnvironmental scienceGeotechnical engineeringMeteorologyOceanographyPhysicsOptics

Abstract

fetched live from OpenAlex

A surface wave model using three nested grids is applied to the eastern end of Lake Ontario to investigate wave propagation from an open lake environment to a small craft harbour protected by a breakwater. The Simulating WAves Nearshore (SWAN) spectral wave model, coupled with the Delft3D hydrodynamic model, is applied to simulate a series of storms in November, 2013. The model results are compared to observations from two pressure sensors, and used to quantify wave properties around existing and future breakwaters to evaluate the bulk changes to the harbour configuration. Overall, the results indicate that the rubblemound breakwater reduces wave heights in the existing harbour by 63% compared to no breakwater, and that the addition of a surface breakwater extension could reduce wave heights by an additional 54%. Wave height attenuation was found to be highly dependent on the incident wave direction relative to breakwater orientation. The spectral wave model is useful for simulating wave transformation for broad directional spectra in wind-sea conditions over large scales to semi-protected areas such as small craft harbours.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.192
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2016
Admission routes3
Has abstractyes

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